Correctly configure finetuning unit test

tests/test_finetuning.py

@@ -1,4 +1,5 @@
 from unittest.mock import patch
+import importlib
 
 from torch.utils.data.dataloader import DataLoader
 
@@ -11,7 +12,7 @@ from llama_recipes.finetuning import main
 @patch('llama_recipes.finetuning.optim.AdamW')
 @patch('llama_recipes.finetuning.StepLR')
 def test_finetuning_no_validation(step_lr, optimizer, get_dataset, tokenizer, get_model, train):
-    kwargs = {"run_validation": True}
+    kwargs = {"run_validation": False}
     
     get_dataset.return_value = [1]
     
@@ -36,8 +37,7 @@ def test_finetuning_no_validation(step_lr, optimizer, get_dataset, tokenizer, ge
 @patch('llama_recipes.finetuning.optim.AdamW')
 @patch('llama_recipes.finetuning.StepLR')
 def test_finetuning_with_validation(step_lr, optimizer, get_dataset, tokenizer, get_model, train):
-    kwargs = {"run_validation": False}
-    
+    kwargs = {"run_validation": True}
     get_dataset.return_value = [1]
     
     main(**kwargs)
@@ -47,7 +47,6 @@ def test_finetuning_with_validation(step_lr, optimizer, get_dataset, tokenizer,
     args, kwargs = train.call_args
     train_dataloader = args[1]
     eval_dataloader = args[2]
-    
     assert isinstance(train_dataloader, DataLoader)
     assert isinstance(eval_dataloader, DataLoader)